A dataset for connecting similar past and present causalities
In this data article, we present a dataset that includes past causalities and categories to connect similar past and present causalities. First, we collect past causalities by referencing certain well-known Japanese high-school textbooks. Subsequently, we select 138 causalities that are useful for a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-04-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920300792 |
| _version_ | 1811208147711295488 |
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| author | Ryohei Ikejiri Yasunobu Sumikawa |
| author_facet | Ryohei Ikejiri Yasunobu Sumikawa |
| author_sort | Ryohei Ikejiri |
| collection | DOAJ |
| description | In this data article, we present a dataset that includes past causalities and categories to connect similar past and present causalities. First, we collect past causalities by referencing certain well-known Japanese high-school textbooks. Subsequently, we select 138 causalities that are useful for analogizing from the causalities to considering solutions for confront present social issues. To enhance the analogy, we describe each causality in three contexts: background including problems, solution methods, and their results. We define 13 categories based on the selected causalities and Encyclopedia of Historiography. The past causalities belong to more than one category. In addition, to train machine learning models including classifier, we collect 900 past events from Wikipedia, and assign one or more categories to the past event data. We perform statistical analyses to understand the quality of the dataset. The proposed applications of the dataset include training machine learning models such as classifiers for past causalities and information retrieval for ranking present social issues according to the similarities between the present and past causalities. Keywords: Digital history, Event category, Text classification, Temporal classification, Information retrieval |
| first_indexed | 2024-04-12T04:16:54Z |
| format | Article |
| id | doaj.art-a676093bfb64492c8af055cfe2d54aac |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-12T04:16:54Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-a676093bfb64492c8af055cfe2d54aac2022-12-22T03:48:22ZengElsevierData in Brief2352-34092020-04-0129A dataset for connecting similar past and present causalitiesRyohei Ikejiri0Yasunobu Sumikawa1The University of Tokyo, JapanTokyo Metropolitan University, Japan; Corresponding author.In this data article, we present a dataset that includes past causalities and categories to connect similar past and present causalities. First, we collect past causalities by referencing certain well-known Japanese high-school textbooks. Subsequently, we select 138 causalities that are useful for analogizing from the causalities to considering solutions for confront present social issues. To enhance the analogy, we describe each causality in three contexts: background including problems, solution methods, and their results. We define 13 categories based on the selected causalities and Encyclopedia of Historiography. The past causalities belong to more than one category. In addition, to train machine learning models including classifier, we collect 900 past events from Wikipedia, and assign one or more categories to the past event data. We perform statistical analyses to understand the quality of the dataset. The proposed applications of the dataset include training machine learning models such as classifiers for past causalities and information retrieval for ranking present social issues according to the similarities between the present and past causalities. Keywords: Digital history, Event category, Text classification, Temporal classification, Information retrievalhttp://www.sciencedirect.com/science/article/pii/S2352340920300792 |
| spellingShingle | Ryohei Ikejiri Yasunobu Sumikawa A dataset for connecting similar past and present causalities Data in Brief |
| title | A dataset for connecting similar past and present causalities |
| title_full | A dataset for connecting similar past and present causalities |
| title_fullStr | A dataset for connecting similar past and present causalities |
| title_full_unstemmed | A dataset for connecting similar past and present causalities |
| title_short | A dataset for connecting similar past and present causalities |
| title_sort | dataset for connecting similar past and present causalities |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920300792 |
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